System and method of computed tomography signal restoration via noise reduction

ABSTRACT

An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. patentapplication Ser. No. 14/132,321, entitled “SYSTEM AND METHOD OF COMPUTEDTOMOGRAPHY SIGNAL RESTORATION VIA NOISE REDUCTION”, filed Dec. 18, 2013,which is herein incorporated by reference in its entirety for allpurposes.

BACKGROUND

This disclosure relates generally to diagnostic imaging and, moreparticularly, to an apparatus and method of restoring signals incomputed tomography (CT) imaging system via noise reduction.

Typically, in computed tomography (CT) imaging systems, an x-ray sourceemits a fan or cone-shaped beam toward a subject or object, such as apatient or a piece of luggage. Hereinafter, the terms “subject” and“object” shall include anything capable of being imaged. The beam, afterbeing attenuated by the subject, impinges upon an array of x-raydetectors. The intensity of the attenuated beam radiation received atthe detector array is typically dependent upon the attenuation of thex-ray beam by the subject. Each detector element of the detector arrayproduces a separate electrical signal indicative of the attenuated beamreceived by each detector element. The electrical signals aretransmitted to a data processing system for analysis which ultimatelyproduces an image.

Generally, the x-ray source and the detector array are rotated about thesubject within an imaging plane and around the subject. X-ray sourcestypically include x-ray tubes, which emit the x-ray beam at a focalpoint. X-ray detectors typically include a collimator for collimatingx-ray beams received at the detector, a scintillator for convertingx-rays to light energy adjacent to the collimator, and photodiodes forreceiving the light energy from the adjacent scintillator and producingelectrical signals therefrom. Typically, each scintillator of ascintillator array converts x-rays to light energy. Each scintillatordischarges light energy to a photodiode adjacent thereto. Eachphotodiode detects the light energy and generates a correspondingelectrical signal. The outputs of the photodiodes are transmitted to thedata processing system for image reconstruction. Imaging data may beobtained using x-rays that are generated at a single polychromaticenergy. However, some systems may obtain multi-energy images thatprovide additional information for generating images.

During scanning to acquire projection data, it is generally desirable toreduce x-ray dose received by the subject, thus protocols have beendeveloped that reduce x-ray tube power and patient exposure during imagedata acquisition. Also, gantry speeds in CT imaging generally continueto increase over time, in an effort to capture images in a shorter timeperiod to reduce motion artifacts. Thus, as dose is reduced and asgantry speed increases, the general trend is to reconstruct images usinglower amounts of photons passing through the image volume, resulting ina reduced signal-to-noise ratio (SNR). As such, the effect ofstatistical noise has thereby increased, resulting in an increasedpropensity for noise-induced artifacts. Thus, there is a need to accountfor statistical noise in CT scanners.

To account for noise, signal restoration has traditionally beenperformed using closed-form or iterative solutions that are essentiallybased on neighbor pixels. For instance, in a known closed-form solution,signal restoration is performed through a weighted average of itsneighbor pixels, using linear or non-linear noise filtering or smoothingalgorithms such as Gaussian smoothing, bi-lateral filtering, and thelike. In a known iterative solution, noise is estimated using aniterative “cost-optimization” approach in which the noise is iterativelyestimated based on the surrounding pixels.

A disadvantage of such methods, however, is that while noise is averagedout, the contrast among neighboring pixels is also averaged out. Thus,when these known methods are applied to signals having a low SNR, ablurred version of the original signal can result.

Therefore, it would be desirable to improve the estimate of statisticalnoise without blurring the original signal.

BRIEF DESCRIPTION

Embodiments are directed toward a method and apparatus of de-noising andrestoring signals in a computed tomography (CT) system in medicalimaging.

According to one aspect, an imaging system includes a computerprogrammed to estimate noise in computed tomography (CT) imaging data,correlate the noise estimation with neighboring CT imaging data togenerate a weighting estimation based on the correlation, de-noise theCT imaging data based on the estimation and on the weighting, andreconstruct an image using the de-noised CT imaging data.

According to another aspect, a method of imaging data processingincludes estimating noise in computed tomography (CT) imaging data,correlating the noise estimation with neighboring CT imaging data,generating a weighting estimation based on the correlation, de-noisingthe CT imaging data based on the estimated noise and on the weightingestimation, and reconstructing an image using the de-noised CT imagingdata.

According to yet another aspect, a non-transitory computer readablestorage medium having stored thereon a computer program comprisinginstructions, which, when executed by a computer, cause the computer toestimate noise in computed tomography (CT) imaging data, correlate thenoise estimation with neighboring CT imaging data to generate aweighting estimation based on the correlation, de-noise the CT imagingdata based on the estimation and on the weighting, and reconstruct animage using the de-noised CT imaging data.

Various other features and advantages will be made apparent from thefollowing detailed description and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial view of a CT imaging system.

FIG. 2 is a block schematic diagram of the system illustrated in FIG. 1.

FIG. 3 is a perspective view of one embodiment of a CT system detectorarray.

FIG. 4 is a perspective view of one embodiment of a detector.

FIG. 5 is a process or flow diagram of a CT image formation process thatincorporates the disclosed subject matter.

FIG. 6 illustrates a de-noising process that may be implemented inprojection space or imaging space, and may be incorporated into thediagram of FIG. 5.

FIGS. 7A and 7B illustrate planar views that represent illustrativeexamples of statistics in image space that may be used to improve noisecorrelation.

FIG. 8 is a pictorial view of a CT system for use with a non-invasivepackage inspection system.

DETAILED DESCRIPTION

The operating environment of disclosed embodiments is described withrespect to a sixty-four-slice computed tomography (CT) system. However,it will be appreciated by those skilled in the art that disclosedembodiments are equally applicable for use with other multi-sliceconfigurations, or other imaging systems in general, such as an x-raysystem on a c-arm or a micro-CT system, as examples. Moreover, disclosedembodiments will be described with respect to the detection andconversion of x-rays. However, one skilled in the art will furtherappreciate that embodiments are equally applicable for the detection andconversion of other high frequency electromagnetic energy. Disclosedembodiments will be described with respect to a “third generation” CTscanner, but is equally applicable with other CT systems as well asvascular and surgical C-arm systems and other x-ray tomography systems.

Referring to FIGS. 1 and 2, a computed tomography (CT) imaging system 10is shown as including a gantry 12 representative of a “third generation”CT scanner. Gantry 12 has an x-ray source 14 that projects a beam ofx-rays 16 toward a detector assembly or collimator 18 on the oppositeside of the gantry 12. X-ray source 14 includes either a stationarytarget or a rotating target. Detector assembly 18 is formed by aplurality of detectors 20 and data acquisition systems (DAS) 22. Theplurality of detectors 20 sense the projected x-rays that pass through apatient 24, and DAS 22 converts the data to digital signals forsubsequent processing. Each detector 20 produces an analog electricalsignal that represents the intensity of an impinging x-ray beam andhence the attenuated beam as it passes through patient 24. During a scanto acquire x-ray projection data, gantry 12 and the components mountedthereon rotate about a center of rotation.

Rotation of gantry 12 and the operation of x-ray source 14 are governedby a control mechanism 26 of CT system 10. Control mechanism 26 includesan x-ray controller 28 and generator 30 that provides power and timingsignals to x-ray source 14 and a gantry motor controller 32 thatcontrols the rotational speed and position of gantry 12. An imagereconstructor 34 receives sampled and digitized x-ray data from DAS 22and performs high speed image reconstruction. The reconstructed image isapplied as an input to a computer 36 which stores the image in a massstorage device 38.

Computer 36 also receives commands and scanning parameters from anoperator via an operator console 40 that has some form of operatorinterface, such as a keyboard, mouse, voice activated controller, or anyother suitable input apparatus. An associated display 42 allows theoperator to observe the reconstructed image and other data from computer36. The operator supplied commands and parameters are used by computer36 to provide control signals and information to DAS 22, x-raycontroller 28, and gantry motor controller 32. In addition, computer 36operates a table motor controller 44 which controls a motorized table 46to position patient 24 and gantry 12. Particularly, table 46 movespatients 24 through a gantry opening 48 in whole or in part. Acoordinate system 50 for detector assembly 18 defines a patient orZ-axis 52 along which patient 24 is moved in and out of opening 48, agantry circumferential or X-axis 54 along which detector assembly 18passes, and a Y-axis 56 that passes along a direction from a focal spotof x-ray source 14 to detector assembly 18.

X-ray source 14, in accordance with present embodiments, is configuredto emit x-ray beam 16 at one or more energies. For example, x-ray source14 may be configured to switch between relatively low energypolychromatic emission spectra (e.g., at approximately 80 kVp) andrelatively high energy polychromatic emission spectra (e.g., atapproximately 140 kVp). As will be appreciated, x-ray source 14 may alsobe operated so as to emit x-rays at more than two different energies.Similarly, x-ray source 14 may emit at polychromatic spectra localizedaround energy levels (i.e., kVp ranges) other than those listed herein(e.g., 100 kVP, 120 kVP, etc.). Selection of the respective energylevels for emission may be based, at least in part, on the anatomy beingimaged.

In some embodiments x-ray controller 28 may be configured to selectivelyactivate x-ray source 14 such that tubes or emitters at differentlocations within system 10 may be operated in synchrony with one anotheror independent of one another. In certain embodiments discussed herein,the x-ray controller 28 may be configured to provide fast-kVp switchingof x-ray source 14 so as to rapidly switch source 14 to emit X-rays atthe respective polychromatic energy spectra in succession during animage acquisition session. For example, in a dual-energy imagingcontext, x-ray controller 28 may operate x-ray source 14 so that x-raysource 14 alternately emits x-rays at the two polychromatic energyspectra of interest, such that adjacent projections are acquired atdifferent energies (i.e., a first projection is acquired at high energy,the second projection is acquired at low energy, the third projection isacquired at high energy, and so forth). In one such implementation,fast-kVp switching operation performed by x-ray controller 28 yieldstemporally registered projection data. In some embodiments, other modesof data acquisition and processing may be utilized. For example, a lowpitch helical mode, rotate-rotate axial mode, N x M mode (e.g., Nlow-kVp views and M high-kVP views) may be utilized to acquiredual-energy datasets.

Techniques to obtain energy sensitive measurements include: (1) scanwith two distinctive energy spectra and (2) detect photon energyaccording to energy deposition in the detector. Such measurementsprovide energy discrimination and material characterization, and may beused to generate reconstructed images using a base materialdecomposition (BMD) algorithm. A conventional BMD algorithm is based onthe concept that, in an energy region for medical CT, the x-rayattenuation of any given material can be represented by a proper densitymix of two materials with distinct x-ray attenuation properties,referred to as the base or basis materials. The BMD algorithm computestwo CT images that represent the equivalent density of one of the basematerials based on the measured projections at high and low x-ray photonenergy spectra, respectively.

Thus, CT image data is obtained that may be from a single or a dualenergy application. CT reconstruction is generally a two-step process.The patient is placed on the scanner and an x-ray beam is caused torotate about the patient, either in a helical or an axial operation.Detectors measure the pattern of radiation (projection) transmittedthrough the patient. Image reconstruction from the projections isperformed using a filtered backprojection (FBP).

As shown in FIG. 3, detector assembly 18 includes rails 300 havingcollimating blades or plates 302 placed therebetween. Plates 302 arepositioned to collimate x-rays 16 before such beams impinge upon, forinstance, detector 20 of FIG. 4 positioned on detector assembly 18. Inone embodiment, detector assembly 18 includes fifty-seven detectors 20,each detector 20 having an array size of 64×16 of pixel elements 400. Asa result, detector assembly 18 has sixty-four rows and nine hundredtwelve columns (16×57 detectors) which allows sixty-four simultaneousslices of data to be collected with each rotation of gantry 12.

Referring to FIG. 4, detector 20 includes DAS 22, with each detector 20including a number of detector elements 400 arranged in pack 402.Detectors 20 include pins 404 positioned within pack 402 relative todetector elements 400. Pack 402 is positioned on a backlit diode array406 having a plurality of diodes 408. Backlit diode array 406 is in turnpositioned on multi-layer substrate 410. Spacers 412 are positioned onmulti-layer substrate 410. Detector elements 400 are optically coupledto backlit diode array 406, and backlit diode array 406 is in turnelectrically coupled to multi-layer substrate 410. Flex circuits 414 areattached to face 416 of multi-layer substrate 410 and to DAS 22.Detectors 20 are positioned within detector assembly 18 by use of pins404.

Referring to FIG. 5, a process or flow diagram 500 is illustrated of aCT image formation process that incorporates the disclosed subjectmatter. Starting at step 502, raw projection data is obtained at step504 and preprocessed at step 506. Preprocessing step 506 includes but isnot limited to such steps as source and detector calibration, accountingfor patient-induced imperfections, and the like. At step 508, imagingdata is de-noised in projection space, according to disclosedembodiments as will be further described. At step 510 and as commonlyknown, a “minus logarithm” step is applied in projection space whichprovides an integrated sum of attenuations of materials through whichx-rays pass, resulting in a line integral along the path of respectivex-rays. In one embodiment, at step 512 a mean preservation or correctionis applied that accounts for a shift in the mean values that may occuras a result of the de-noising step 508. It is contemplated, in oneembodiment, that an air calibration step is performed, typically at theend of the preprocessing step 506 and before the noise reduction step508.

Image reconstruction occurs at step 514 which, in one embodiment, is aknown tomographic reconstruction technique such as a filteredbackprojection (FBP). However, it is contemplated that other imagereconstruction techniques may be used as well. According to anembodiment of the disclosure, at step 516 imaging data is de-noised inimage space, according to disclosed embodiments and as will be furtherdescribed. At step 518, the images are post-processed, to include suchsteps as correcting for imaging artifacts. The process ends at step 520.

Referring to FIG. 6, CT imaging data is de-noised in either projectionspace (step 508) or in image space (516). A process or flow diagram 600is illustrated having steps that may be applied in projection space,image space, or both, according to disclosed embodiments. Process 600starts at step 602 in which input data at step 604 is obtained and noiseis estimated at step 606, a weighting for the input data is estimated atstep 608, and the input data is de-noised at step 610 based on theestimated noise statistics from step 606 and based on the estimatedweighting at step 608. According to one embodiment, de-noising step 610is performed without iteration, but in other embodiments, de-noisingstep 610 is iterated upon 612 by feeding the de-noised input data backinto process 600, after which noise statistics are again estimated atstep 606 and a weighting is again estimated at step 608, which are thenused to iterate and produce a revised de-noise data at step 610. Process600 may thereby include an iteration step 614 for one or moreiterations, and when a threshold between iterations is reached, if nofurther iteration is performed 616, process 600 ends 618.

As such, according to one embodiment, process 600 includes one or moreiterations that occur at block 614 until convergence occurs or athreshold is met. However, according to another embodiment, process 600does not include an iteration option, thus in this example, at block 614no iteration occurs 616 and the process ends 618.

Process 600 may be applied to de-noise imaging data in projection space(step 508) or in imaging space (516). Generally, therefore, an imagingsystem, such as imaging system 10 of FIG. 1, includes a computer 36programmed to estimate noise in computed tomography (CT) imaging data,correlate the noise estimation with neighboring CT imaging data togenerate a weighting estimation based on the correlation, de-noise theCT imaging data based on the estimation and on the weighting, andreconstruct an image using the de-noised CT imaging data. That is,whether in projection space or image space, steps 602-618 may be appliedto the imaging data to reduce noise and improve the final image, witheach step applicable to the particular space (projection or image). Thede-noising update process to minimize a cost function can be carried outbased on the following equation:

$\begin{matrix}{{\frac{\partial y}{\partial t} = {{div}\left\lbrack {{d\left( {{\nabla y}} \right)} \cdot {\nabla y}} \right\rbrack}};} & {{Eqn}.\mspace{14mu} (1)}\end{matrix}$

where t represents the time sequence or the iteration, ∇ is a gradientoperator, and d is a monotonically decreasing function defined by:

$\begin{matrix}{{{d(z)} = ^{- \frac{\eta \; z^{2}}{\sigma^{2}}}},} & {{Eqn}.\mspace{14mu} (2)}\end{matrix}$

where σ is the data noise and 11 is a parameter that controls thestrength of the update. Noise, σ, is obtained through the accuratemodeling of the noise statistics and is calculated for everymeasurement.

Projection Space

When in projection space and at step 508, the noise reduction process isconducted adaptively based on an estimation of noise behavior of eachprojection measurement. As such, when in projection space the input dataat step 604 is projection data. Noise statistics are estimated at step606, in one example, by estimating the variance of each measurementdirectly from the X-ray count using an approximate or assumed compoundPoisson distribution. That is, and as commonly known, in this example adiscrete probability distribution expresses the probability of a givennumber of events occurring in a fixed interval of time and/or space.

In order to do this, raw electron counts are converted back to x-raycounts. Note that, in an example using energy integration detectors,conversion factors can be experimentally determined for different energyspectra offered on the system. Signal-to-noise ratio (SNR) values arethen formed for each measurement using the ratio of the measurementvalue over the noise standard deviation estimation. The SNR valuesestimated for each measurement may be used as the basis for the strengthof the noise reduction for the measurement.

To facilitate the removal of the noise, estimation of a weighting atstep 608 is based on at least one of a view, a channel, and a row of CTimaging data in projection space, and based on a correlation with aneighboring pixel or patch of pixels. That is, when in projection space,similarity information as it pertains to sinogram data is used toimprove the estimation of the noise by taking advantage of the fact thatnoise may be correlated within the sinogram and for similar datatherein. If a strong correlation is found, then larger weights can beapplied at step 608. This similarity measurement could be implemented inthe manner of pixels or patches. That is, one can simply compare thedifference between two pixels or two patches centered at the two pixels.The differences will be grouped together to normalize and calculate thecorresponding weights for similarity measure.

Note that for the sinogram process, the similarity measure should becomputed after the negative log step 510 to ensure linearity of themeasurement. At step 512, a mean preservation step is introduced tofurther ensure that the mean of the projection data is not shifted bythe noise reduction process.

And, as summarized in FIG. 6, at block 614 the process may be iteratedupon by cycling back through the steps to further decrease noise untilconvergence occurs, until a threshold difference of de-noised data isminimized, or for a fixed number of steps. The de-noising process isdesigned to compress noise while maintaining the signal, especially withrespect to the edge of the signal. An edge preservation property isintegrated in the process to ensure that the noise reduction is stoppedwhen reaching an object edge, and resumes along the direction of theedge.

The neighboring pixels with a larger similarity measure and higher SNRwill be weighted more in the process. Further, the de-noising processcan use any type of edge and mean preserving noise reduction method. Ashas been discussed, the projection space de-noising is done based ondirect noise estimation from the x-ray counts. The smaller the count,the larger the de-noising strength.

Image Space

When in image space and at step 508, (referring again to FIG. 6, butthis time in the context of image space), the noise reduction process isconducted adaptively based on an estimation of noise behavior of imagevoxels. As such, when in image space the input data at step 604 isreconstructed image data. Noise statistics are estimated at step 606, inone example, by obtaining the image space noise statistic using theimage space process.

To facilitate the removal of the noise, estimation of a weighting atstep 608 is based on an estimation of a 3D image volume measure alongpixels or patches of data. That is, when in image space, similarityinformation as it pertains to x-y-z voxel information, slice thickness,and the like, is used to improve the estimation of the noise by takingadvantage of the fact that noise may be correlated within the imagevolume and for similar data therein. If a strong correlation is found,then larger weights can be applied at step 608. This similaritymeasurement could be implemented in the manner of pixels or patches.That is, one can simply compare the difference between two pixels or twopatches centered at the two pixels. The differences will be groupedtogether to normalize and calculate the corresponding weights forsimilarity measure.

FIGS. 7A and 7B illustrate planar views that represent examples ofstatistics in image space that may be used to improve noise correlation.FIG. 7A, for instance, illustrates a wire 700 and a correspondingportion 702 of reconstructed imaging data in which pixels 704 correlateto wire 700. As such, the reconstruction computational complexity ofpixels 704 are comparable with respect to each other, and given theknowledge that pixels 704 correspond to wire 700, it is contemplatedthat the weighting determined and applied at step 608 is therebycomparable to its neighboring pixel. FIG. 7B, as another example,includes three wires 706 having patches 708 that therefore also havecomparable computational complexity and, given their approximatecorrelation to one another, may include similarity weighting at step 608as well. Thus, in such examples, knowledge of the image can be used toaugment the weighting based on pixel or patch similarity.

And, as summarized in FIG. 6, at block 614 the process may be iteratedupon by cycling back through the steps to further decrease noise untilconvergence occurs, until a threshold difference of de-noised data isminimized, or for a fixed number of steps. The de-noising process isdesigned to compress noise while maintaining the signal, especially withrespect to the edge of the signal. An edge preservation property isintegrated in the process to ensure that the noise reduction is stoppedwhen reaching an object edge, and resumes along the direction of theedge.

The neighboring pixels with a larger similarity measure and higher SNRwill be weighted more in the process. Further, the de-noising processcan use any type of edge and mean preserving noise reduction method. Ashas been discussed, the image space de-noising is done based on noiseestimation from the image space process. The larger the noiseestimation, the larger the de-noising strength.

The de-noising process can be done independently in projection(sinogram) space, as well as the image space, and can be done togetherand iteratively.

Referring now to FIG. 8, there is shown a package/baggage inspectionsystem 1000 that can use the image acquisition and reconstructionstechniques according to embodiments disclosed and which includes arotatable gantry 1002 having an opening 1004 therein through whichpackages or pieces of baggage may pass. The rotatable gantry 1002 housesone or more x-ray energy sources 1006 as well as a detector assembly1008 having scintillator arrays comprised of scintillator cells. Aconveyor system 1010 is also provided and includes a conveyor belt 1012supported by structure 1014 to automatically and continuously passpackages or baggage pieces 1016 through opening 1004 to be scanned.Objects 1016 are passed through opening 1004 by conveyor belt 1012,imaging data is then acquired, and the conveyor belt 1012 removes thepackages 1016 from opening 1004 in a controlled and continuous manner.As a result, postal inspectors, baggage handlers, and other securitypersonnel may non-invasively inspect the contents of packages 1016 forexplosives, knives, guns, contraband, etc.

An implementation of system 10 and/or 1000 in an example comprises aplurality of components such as one or more of electronic components,hardware components, and/or computer software components. A number ofsuch components can be combined or divided in an implementation of thesystem 10 and/or 1000. An exemplary component of an implementation ofthe system 10 and/or 1000 employs and/or comprises a set and/or seriesof computer instructions written in or implemented with any of a numberof programming languages, as will be appreciated by those skilled in theart. An implementation of system 10 and/or 1000 in an example comprisesany (e.g., horizontal, oblique, or vertical) orientation, with thedescription and figures herein illustrating an exemplary orientation ofan implementation of the system 10 and/or 1000, for explanatorypurposes.

An implementation of system 10 and/or system 1000 in an example employsone or more computer readable signal bearing media. A computer-readablesignal-bearing medium in an example stores software, firmware and/orassembly language for performing one or more portions of one or moreimplementations. An example of a computer-readable signal-bearing mediumfor an implementation of the system 10 and/or the system 1000 comprisesthe recordable data storage medium of the image reconstructor 34, and/ormass storage device 38 of computer 36. A computer-readablesignal-bearing medium for an implementation of the system 10 and/or thesystem 1000 in an example comprises one or more of a magnetic,electrical, optical, biological, and/or atomic data storage medium. Forexample, an implementation of the computer-readable signal-bearingmedium comprises floppy disks, magnetic tapes, CD-ROMs, DVD-ROMs, harddisk drives, and/or electronic memory. In another example, animplementation of the computer-readable signal-bearing medium comprisesa modulated carrier signal transmitted over a network comprising orcoupled with an implementation of the system 10 and/or the system 1000,for instance, one or more of a telephone network, a local area network(“LAN”), a wide area network (“WAN”), the Internet, and/or a wirelessnetwork.

According to one embodiment, an imaging system includes a computerprogrammed to estimate noise in computed tomography (CT) imaging data,correlate the noise estimation with neighboring CT imaging data togenerate a weighting estimation based on the correlation, de-noise theCT imaging data based on the estimation and on the weighting, andreconstruct an image using the de-noised CT imaging data.

According to another embodiment, a method of imaging data processingincludes estimating noise in computed tomography (CT) imaging data,correlating the noise estimation with neighboring CT imaging data,generating a weighting estimation based on the correlation, de-noisingthe CT imaging data based on the estimated noise and on the weightingestimation, and reconstructing an image using the de-noised CT imagingdata.

According to yet another embodiment, a non-transitory computer readablestorage medium having stored thereon a computer program comprisinginstructions, which, when executed by a computer, cause the computer toestimate noise in computed tomography (CT) imaging data, correlate thenoise estimation with neighboring CT imaging data to generate aweighting estimation based on the correlation, de-noise the CT imagingdata based on the noise estimation and on the weighting, and reconstructan image using the de-noised CT imaging data.

A technical contribution for the disclosed method and apparatus is thatit provides for a computer-implemented apparatus and method ofde-noising and restoring signals in computed tomography (CT) image data.

When introducing elements of various embodiments of the disclosedmaterials, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Furthermore, any numerical examples in the following discussion areintended to be non-limiting, and thus additional numerical values,ranges, and percentages are within the scope of the disclosedembodiments.

While the preceding discussion is generally provided in the context ofmedical imaging, it should be appreciated that the present techniquesare not limited to such medical contexts. The provision of examples andexplanations in such a medical context is to facilitate explanation byproviding instances of implementations and applications. The disclosedapproaches may also be utilized in other contexts, such as thenon-destructive inspection of manufactured parts or goods (i.e., qualitycontrol or quality review applications), and/or the non-invasiveinspection of packages, boxes, luggage, and so forth (i.e., security orscreening applications).

While the disclosed materials have been described in detail inconnection with only a limited number of embodiments, it should bereadily understood that the embodiments are not limited to suchdisclosed embodiments. Rather, that disclosed can be modified toincorporate any number of variations, alterations, substitutions orequivalent arrangements not heretofore described, but which arecommensurate with the spirit and scope of the disclosed materials.Furthermore, while single energy and dual-energy techniques arediscussed above, that disclosed encompasses approaches with more thantwo energies. Additionally, while various embodiments have beendescribed, it is to be understood that disclosed aspects may includeonly some of the described embodiments. Accordingly, that disclosed isnot to be seen as limited by the foregoing description, but is onlylimited by the scope of the appended claims.

What is claimed is:
 1. A multi-energy computed tomography (CT) imagingsystem comprising a computer programmed to: for a data acquisition at afirst energy spectrum, convert raw electron counts to correspondingX-ray counts using a conversion factor determined for the first energyspectrum; estimate a measure of noise for the data acquisition byestimating the variance of each acquired measurement directly from theX-ray counts; generate a weighting estimation based on the correlationbetween the measure of noise and neighboring projection measurementdata; de-noise the projection data corresponding to the data acquisitionbased on the weighting estimation; and reconstruct an image using thede-noised CT projection data.
 2. The multi-energy computed tomography(CT) imaging system of claim 1, wherein the computer is furtherprogrammed to: for a second data acquisition as a second energyspectrum, convert raw electron counts to corresponding X-ray countsusing a second conversion factor for the second energy spectrum.
 3. Themulti-energy computed tomography (CT) imaging system of claim 2, whereinthe computer is further programmed to: estimate a second measure ofnoise for the second data acquisition by estimating the variance of eachacquired measurement directly from the X-ray counts associated with thesecond data acquisition; generate a second weighting estimation based onthe correlation between the second measure of noise and neighboringprojection measurement data for the second data acquisition; andde-noise the projection data corresponding to the second dataacquisition based on the second weighting estimation.
 4. Themulti-energy computed tomography (CT) imaging system of claim 2, whereinthe computer is further programmed to: estimate a second measure ofnoise for the second data acquisition by estimating the variance of eachacquired measurement directly from the X-ray counts associated with thesecond data acquisition; and generate the weighting estimation based onthe correlation between the measure of noise and neighboring projectionmeasurement data based on a combination of the measure of noise andsecond measure of noise.
 5. The multi-energy computed tomography (CT)imaging system of claim 1, wherein the computer is further programmedto: perform corresponding data acquisitions at one or more additionalenergy spectra; estimate the measure of noise for the first energyspectrum and the one or more additional energy spectra; and generate theweighting estimation based on the correlation between the measure ofnoise and neighboring projection measurement data for the first energyspectrum and the one or more additional energy spectra.
 6. Themulti-energy computed tomography (CT) imaging system of claim 1, whereinthe computer is further programmed to estimate the variance based on anassumed Poisson distribution.
 7. The multi-energy computed tomography(CT) imaging system of claim 1, wherein the computer is furtherprogrammed to correlate between the measure of noise and neighboringprojection measurement data based on at least one of a view, a channel,and a row of CT imaging data in projection space.
 8. The multi-energycomputed tomography (CT) imaging system of claim 1, wherein the computeris further programmed to: generate an estimate of noise in image dategenerated form the de-noised CT projection data; and de-noise the imagedata based on the estimate of noise in the image data.
 9. Themulti-energy computed tomography (CT) imaging system of claim 1, whereinthe computer is further programmed to correlate the measure of noise ina 3D image volume measured among one of a neighboring pixel or aneighboring patch of pixels.
 10. A computed tomography (CT) imagingsystem comprising a computer programmed to: for a CT scan, estimate ameasure of noise for each projection measurement of the CT scan;generate a weighting estimation for at least one of a view, a channel,or a row of the projection data by correlating the respective projectionmeasurements with a neighboring pixel or patch of pixels; de-noise theprojection data corresponding to the data acquisition based on theweighting estimation; and reconstruct an image using the de-noised CTprojection data.
 11. The computed tomography (CT) imaging system ofclaim 10, wherein the projection measurements are acquired at two ormore energy spectra and a separate measure of noise is estimated foreach energy spectrum.
 12. The computed tomography (CT) imaging system ofclaim 11, wherein the weighting estimation is determined separately foreach energy spectrum.
 13. The computed tomography (CT) imaging system ofclaim 11, wherein the weighting estimation is determined jointly usingthe measures of noise estimated for each energy spectrum.
 14. Thecomputed tomography (CT) imaging system of claim 11, wherein thecomputer is further programmed to estimate the measure of noise based ona variance of each projection measurement using an X-ray count.
 15. Thecomputed tomography (CT) imaging system of claim 14, wherein thecomputer is further programmed to, estimate the variance based on anassumed Poisson distribution.
 16. A computed tomography (CT) imagingsystem comprising a computer programmed to: reconstruct an image from aset of acquired projection measurements; estimate a measure of noise fora plurality of voxels of the image; generate weighting estimations basedon a correlation between respective measures of noise and similar imagedata, wherein the similarity of the image data is based on one or moreof voxel coordinates and slice thickness; and de-noise the image basedon the weighting estimation.
 17. The computed tomography (CT) imagingsystem of claim 16, wherein the similarity of image data is based onneighboring pixels or patches of pixels in a 3D image volume.
 18. Thecomputed tomography (CT) imaging system of claim 17, wherein thesimilarity is determined based on the difference between two pixels ortwo patches of pixels.
 19. The computed tomography (CT) imaging systemof claim 16, wherein the computer is further programmed to iterativelyperform the steps of estimating the measure of noise, generate weightingestimations, and de-noise the image until a threshold condition is met.20. The computed tomography (CT) imaging system of claim 16, wherein thecomputer is further programmed to apply an edge preservation criteria aspart of the de-noising operation.